Satisfaction Data Collected by E-mail and Smartphone for Emergency Department Patients: How Do Responders Compare With Nonresponders?

Objective: This study profiles an innovative approach to capture patient satisfaction data from emergency department (ED) patients by implementing an electronic survey method. This study compares responders to nonresponders.

Background: Our hypothesis is that the cohort of survey respondents will be similar to nonresponders in terms of the key characteristics of age, gender, race, ethnicity, ED disposition, and payor status.

A flexible simulation platform to quantify and manage emergency department crowding.

Background: Hospital-based Emergency Departments are struggling to provide timely care to a steadily increasing number of unscheduled ED visits. Dwindling compensation and rising ED closures dictate that meeting this challenge demands greater operational efficiency.

Methods: Using techniques from operations research theory, as well as a novel event-driven algorithm for processing priority queues, we developed a flexible simulation platform for hospital-based EDs.

A pseudo-penalized quasi-likelihood approach to the spatial misalignment problem with non-normal data.

Spatially referenced datasets arising from multiple sources are routinely combined to assess relationships among various outcomes and covariates. The geographical units associated with the data, such as the geographical coordinates or areal-level administrative units, are often spatially misaligned, that is, observed at different locations or aggregated over different geographical units. As a result, the covariate is often predicted at the locations where the response is observed.

Estimated generalized least squares in spatially misaligned regression models with Berkson error.

In environmental studies, relationships among variables that are misaligned in space are routinely assessed. Because the data are misaligned, kriging is often used to predict the covariate at the locations where the response is observed. Using kriging predictions to estimate regression parameters in linear regression models introduces a Berkson error, which induces a covariance structure that is challenging to estimate.

Place-based attributes predict community membership in a mobile phone communication network.

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities.

RNA-seq: technical variability and sampling.

Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported.

A comparison of errors in variables methods for use in regression models with spatially misaligned data.

When a response variable Y is measured on one set of points and a spatially varying predictor variable X is measured on a different set of points, X and Y have different supports and thus are spatially misaligned. To draw inference about the association between X and Y , X is commonly predicted at the points for which Y is observed, and Y is regressed on the predicted X. If X is predicted using kriging or some other smoothing approach, use of the predicted instead of the true (unobserved) X values in the regression results in unbiased estimates of the regression parameters.